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How Quinnox Helped Build a Gen-AI Powered Fragrance Formulation Recommendation Engine

AWS platform

Overview

The client is a leading fragrance company known for creating high-quality scents for various applications for various global brands. As part of the product life cycle, the first step that the company followed is a project brief from the prospect, which is manually analyzed by evaluators for matching formulations from internal libraries. However, the process they followed was time-consuming and allowed the company to respond to only 30-40% of the project briefs.

Business Need

The client envisioned a Gen-AI-based formulation recommendation engine to enhance the response rate for incoming project briefs and streamline the process.

The Quinnox Solution

  • The solution was developed on the Azure platform, integrating with the main PLM application built using Power Apps.
  • It utilized LLMs and RAG techniques to analyze each incoming brief and provide system-generated recommendations for the best match, including compositions and dosages.
  • The RAG architecture was leveraged to ingest regulatory documents, swiftly parse them, and extract only the critical aspects. This streamlined approach enabled quick comprehension and action, eliminating the need for lengthy manual interpretation.

Tech Used: Langchain, Python, Azure Open AI services, Azure Functions

Business Benefits Delivered

  1. Increased Efficiency: Overall improvements by 60-70% on the number of project briefs which were sent back to prospective clients.
  2. Real-Time Compliance: Ensures fragrances meet legal requirements by cross-referencing compositions with regulations instantly

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